Journal
FRONTIERS IN NEUROSCIENCE
Volume 6, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2012.00166
Keywords
amygdala; automated segmentation; structural MRI; amygdala volume; Freesurfer; medial temporal lobe; diffeomorphic warping; hand-tracing
Categories
Funding
- US National Institute of Mental Health [MH044340, MH043454, MH084051, MH61285, MH68858]
- US National Institute on Aging [AG037376, AG027785]
- US National Institute of Drug Abuse [DA028087]
- Eunice Kennedy Shriver US National Institute of Child Health and Human Development [HD003352-46]
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [P30HD003352] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R37MH043454, R01MH043454, R01MH068858, P50MH084051, R01MH061285] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [K25AG027785, R01AG037376] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DRUG ABUSE [F31DA028087] Funding Source: NIH RePORTER
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Here, we describe a novel method for volumetric segmentation of the amygdala from M RI images collected from 35 human subjects. This approach is adapted from open-source techniques employed previously with the hippocampus (Seh et al., 2011; Wang et al., 2011a,b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high Dice (Mean = 0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean = 0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class correlations (consistency = 0.830, absolute agreement = 0.819 for the left; consistency = 0.786, absolute agreement = 0.783 for the right) and bivariate (r = 0.831 for the left; r = 0.797 for the right) compared to hand-drawn amygdala. Our results are discussed in relation to other cutting-edge segmentation techniques, as well as commonly available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed.
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